Occupation-Level Variance
Occupation codes changed between 2010 and 2018 Census vintages. This analysis harmonizes both into a single frame and examines within-occupation earnings dispersion by gender across ~500 occupations and 11 ACS years (2013–2024, pooled 9.7M observations).
Sample window note: This page pools ACS 2013–2024 to maximize occupation cell sizes. The headline trend series covers 2015–2023. Results here are a separate pooled extension, not directly comparable to the year-by-year adjusted gap estimates.
How to Read This
Variance ratio > 1: male earnings are more dispersed than female in that occupation.
Variance ratio < 1: female earnings are more dispersed than male.
Top-decile gap < 0: men account for a larger share of the top earnings decile.
Top-decile gap > 0: women account for a larger share of the top earnings decile.
All variance ratios are male / female (M/F), matching the
Variance page and standard literature convention.
All results are descriptive summaries of the earnings distribution, not causal estimates.
Small cell sizes (marked N<1K) increase noise in individual occupation estimates.
Harmonization types: 2018 native 2018 code mixed 1:1 crosswalk split 1-to-many split bucket 2010 legacy 2010 only
Variance Leaderboards
Top 10 Each DirectionFemale-Higher-Variance Occupations (Annual)
Variance ratio (M/F) — lowest 10 (female more dispersed)
Female earnings are more dispersed than male earnings in these occupations. Source: results/variance/acs_occupation_variability_leaders.csv
| Occupation | Ratio (M/F) | Residual | SOC | N | Type |
|---|---|---|---|---|---|
| Agents/biz mgrs of artists | 0.13 | 0.06 | Business/Fin. | 2,603 | mixed |
| Misc. vehicle/mobile equip mechanics | 0.21 | 0.23 | Install./Repair | 5,011 | mixed |
| Elevator & escalator installers | 0.25 | 0.25 | Construction | 2,085 | mixed |
| Parts salespersons | 0.26 | 0.19 | Sales | 7,017 | mixed |
| Solar photovoltaic installers | 0.27 | 0.33 | Construction | 645 | 2018 N<1K |
| Metal workers, all other (2010) | 0.27 | 0.25 | Production | 13,392 | 2010 |
| Food/tobacco machine operators | 0.39 | 0.42 | Production | 719 | mixed N<1K |
| Electrical power-line installers | 0.40 | 0.43 | Install./Repair | 9,850 | mixed |
| Furnace/kiln/oven operators | 0.41 | 0.32 | Production | 842 | mixed N<1K |
| Forming machine operators | 0.44 | 0.51 | Production | 1,182 | 2018 |
Male-Higher-Variance Occupations (Annual)
Variance ratio (M/F) — top 10 (male more dispersed)
Male earnings are more dispersed than female earnings in these occupations. Source: results/variance/acs_occupation_variability_leaders.csv
| Occupation | Ratio (M/F) | Residual | SOC | N | Type |
|---|---|---|---|---|---|
| Child/family/school social workers | 2.08 | 2.33 | Community/Social | 3,216 | 2018 |
| Rehabilitation counselors | 2.04 | 1.89 | Community/Social | 952 | 2018 N<1K |
| Production helpers (2010) | 2.04 | 1.92 | Production | 1,271 | split |
| Avionics technicians | 1.92 | 1.54 | Install./Repair | 1,518 | mixed |
| Mental health/substance abuse social workers | 1.82 | 1.52 | Community/Social | 1,116 | 2018 |
| Title examiners/abstractors | 1.82 | 1.41 | Legal | 3,402 | 2018 |
| Paralegals and legal assistants | 1.82 | 1.69 | Legal | 30,142 | mixed |
| Photographers | 1.79 | 2.70 | Arts/Design | 5,416 | mixed |
| Passenger attendants | 1.69 | 1.61 | Transportation | 780 | 2018 N<1K |
| Fundraisers | 1.67 | 1.59 | Business/Fin. | 7,095 | mixed |
Female-Higher-Variance Occupations (Hourly)
Variance ratio (M/F) — lowest 10 (female more dispersed)
Female hourly wages are more dispersed than male in these occupations.
| Occupation | Ratio (M/F) | Residual | SOC | N | Type |
|---|---|---|---|---|---|
| Agents/biz mgrs of artists | 0.25 | 0.11 | Business/Fin. | 2,603 | mixed |
| Cement masons/concrete finishers | 0.30 | 0.27 | Construction | 3,882 | mixed |
| Elevator & escalator installers | 0.35 | 0.35 | Construction | 2,085 | mixed |
| Millwrights | 0.37 | 0.42 | Install./Repair | 3,715 | mixed |
| Metal workers, all other (2010) | 0.37 | 0.29 | Production | 13,392 | 2010 |
| Food/tobacco machine operators | 0.38 | 0.44 | Production | 719 | mixed N<1K |
| Tool and die makers | 0.42 | 0.43 | Production | 3,052 | mixed |
| Helpers, install./repair | 0.43 | 0.43 | Install./Repair | 628 | 2018 N<1K |
| Electrical/electronics engineers | 0.43 | 0.47 | Architecture/Eng. | 17,223 | mixed |
| Other personal appearance workers | 0.47 | 0.45 | Personal Care | 591 | 2018 N<1K |
Male-Higher-Variance Occupations (Hourly)
Variance ratio (M/F) — top 10 (male more dispersed)
Male hourly wages are more dispersed than female in these occupations.
| Occupation | Ratio (M/F) | Residual | SOC | N | Type |
|---|---|---|---|---|---|
| Photographers | 2.86 | 4.17 | Arts/Design | 5,419 | mixed |
| Production helpers (2010) | 2.63 | 2.27 | Production | 1,271 | split |
| Helpers, install./repair (2010) | 2.56 | 2.17 | Install./Repair | 577 | split N<1K |
| Medical transcriptionists | 2.22 | 1.92 | Healthcare Supp. | 2,127 | mixed |
| Control/valve installers | 2.17 | 2.27 | Install./Repair | 1,614 | mixed |
| Explosives workers/blasters | 2.17 | 2.78 | Construction | 635 | 2018 N<1K |
| Library assistants, clerical | 2.13 | 1.92 | Office/Admin. | 5,638 | mixed |
| Marine engineers/naval architects | 2.08 | 2.13 | Architecture/Eng. | 1,084 | mixed |
| Fishing and hunting workers | 1.96 | 2.70 | Farming/Fishing | 660 | 2018 N<1K |
| Credit analysts | 1.96 | 1.96 | Business/Fin. | 2,485 | mixed |
Female earnings are more dispersed in occupations concentrated in production, construction, and installation/repair. Male earnings are more dispersed in occupations concentrated in community/social service, legal, and arts fields. The pattern is consistent with the minority gender in a given occupation tending to show wider earnings spread, though other factors may also contribute.
Top-Decile Concentration
Who Reaches the Top 10%Largest Male Top-Decile Advantage (Annual)
Occupations where men account for the largest share of top-decile earnings
Gap = female top-10% share − male top-10% share (pp). More negative = larger male advantage.
| Occupation | F Top-10% | M Top-10% | Gap (pp) | N |
|---|---|---|---|---|
| Probation officers / correctional | 0.8% | 76.1% | −75.3 | 7,560 |
| Parts salespersons | 4.3% | 77.7% | −73.4 | 7,017 |
| Photographers | 3.6% | 37.3% | −33.7 | 5,416 |
| Dental hygienists | 9.1% | 32.3% | −23.1 | 15,322 |
| Diagnostic medical sonographers | 6.3% | 26.2% | −20.0 | 4,448 |
| Pressers, textile/garment | 3.2% | 22.3% | −19.1 | 2,198 |
| Credit authorizers/checkers | 5.0% | 23.2% | −18.2 | 2,828 |
| Title examiners/abstractors | 5.2% | 23.1% | −17.9 | 3,402 |
| Production helpers (2010) | 2.3% | 19.4% | −17.1 | 1,271 |
Largest Female Top-Decile Advantage (Annual)
Occupations where women account for the largest share of top-decile earnings
Gap = female top-10% share − male top-10% share (pp). More positive = larger female advantage.
| Occupation | F Top-10% | M Top-10% | Gap (pp) | N |
|---|---|---|---|---|
| Agents/biz mgrs of artists | 70.1% | 5.7% | +64.4 | 2,603 |
Only one occupation in the top-25 absolute-gap leaderboard shows a female top-decile advantage. This asymmetry is itself a descriptive finding: across most occupations with large absolute top-decile gaps, men account for a larger share of top earners than women.
Largest Male Top-Decile Advantage (Hourly)
Occupations where men account for the largest share of top-decile hourly wages
| Occupation | F Top-10% | M Top-10% | Gap (pp) | N |
|---|---|---|---|---|
| Probation officers / correctional | 1.0% | 76.1% | −75.1 | 7,560 |
| Parts salespersons | 5.1% | 77.2% | −72.1 | 7,017 |
| Photographers | 4.4% | 33.3% | −28.8 | 5,419 |
| Title examiners/abstractors | 5.5% | 22.4% | −16.9 | 3,402 |
| Dental hygienists | 9.5% | 25.3% | −15.8 | 15,322 |
| Diagnostic medical sonographers | 7.1% | 22.5% | −15.4 | 4,448 |
| Pressers, textile/garment | 4.7% | 19.9% | −15.2 | 2,198 |
| Credit authorizers/checkers | 5.9% | 20.7% | −14.7 | 2,828 |
| Production helpers (2010) | 3.4% | 18.0% | −14.6 | 1,271 |
Largest Female Top-Decile Advantage (Hourly)
Occupations where women account for the largest share of top-decile hourly wages
| Occupation | F Top-10% | M Top-10% | Gap (pp) | N |
|---|---|---|---|---|
| Agents/biz mgrs of artists | 72.0% | 5.5% | +66.5 | 2,603 |
As with annual earnings, only one occupation in the top-25 by absolute gap shows a female advantage in top-decile concentration. In the vast majority of occupations with large absolute gaps, men hold a larger share of the top decile.
SOC Group Concentration
Annual Earnings Top-10 SeatsTop-10 Leaderboard Seats by SOC Major Group
Count of occupations in each top-10 leaderboard, by industry group
Each bar = number of occupations from that SOC group appearing in the given top-10 leaderboard. Annual earnings outcome.
| SOC Group | Female Higher | Male Higher |
|---|---|---|
| Production | 4 | 1 |
| Construction & Extraction | 2 | 0 |
| Installation / Repair | 2 | 1 |
| Community & Social Service | 0 | 3 |
| Legal | 0 | 2 |
| Business / Financial | 1 | 1 |
| Sales | 1 | 0 |
| Arts / Design / Media | 0 | 1 |
| Transportation | 0 | 1 |
Female-higher-variance occupations are concentrated in production, construction, and installation/repair. Male-higher-variance occupations are concentrated in community/social service and legal fields. One possible interpretation is that the gender in the minority within an occupation tends to show wider earnings spread, but other factors — including occupational structure and measurement — may also play a role.
Pre/Post-2020 Variance Regime
Structural Shift CheckHourly Variance Metrics: Pre-2020 vs. Post-2020
Weighted means across ACS years in each regime
Pre-2020: ACS 2013–2019 (N=6.1M). Post-2020: ACS 2021–2024 (N=3.6M). No 2020 ACS available.
| Metric (Hourly) | Pre-2020 | Post-2020 | Direction |
|---|---|---|---|
| Raw variance ratio (M/F) | 1.122 | 1.085 | toward parity |
| Residual variance ratio (M/F) | 1.053 | 1.054 | stable |
| Female top-10% share | 7.47% | 7.82% | increased |
| Male top-10% share | 12.65% | 12.29% | decreased |
| Female top-5% share | 3.47% | 3.75% | increased |
| Male top-5% share | 6.67% | 6.19% | decreased |
Post-2020, hourly dispersion moved modestly toward parity: the within-occupation M/F variance ratio decreased from 1.12 to 1.08, and women gained slightly in top-earner shares. The residual ratio was essentially unchanged. These shifts are consistent with a mild post-pandemic compression of hourly extremes rather than a structural break.
Methods & Caveats
Occupation harmonization: Census occupation codes changed in 2018.
This analysis maps all years onto a unified frame using native 2018 codes, 1:1 crosswalks,
split buckets, and legacy-2010-only buckets. The full mapping is in
results/diagnostics/variance_occupation_harmonization_map.csv.
Small cell sizes: Some leaderboard occupations have fewer than 1,000 pooled observations (marked N<1K). Variance ratios in these cells are noisier — treat individual rankings as indicative.
Fertility-risk bridge: A descriptive bridge
between fertility-risk quartiles and variance metrics is available in
results/variance/acs_fertility_risk_variance_bridge.csv. It has weaker male/female
ratio coverage and is not charted here.
Interpretation: All results are descriptive summaries of observed earnings distributions. Higher or lower variance within an occupation could reflect differences in tenure, specialization, hours composition, hiring patterns, or measurement. These tables identify where dispersion differs, not why.